New Control Method Handles Noisy Sensor Data in Real-Time Systems
Researchers have developed an adaptive tracking control technique that works with imperfect sensor readings and unpredictable command signals—a common challenge in industrial automation and robotics. The advance could improve performance in manufacturing, power grids, and autonomous systems where data compression and measurement noise currently limit precision.
Originaltitel: Adaptive Tracking Control for ARMA Models with Quantized Observations Under Non-Periodic Reference Signals
<p>This paper investigates the adaptive tracking control problem for AutoRegressive Moving Average (ARMA) systems with quantized observations, explicitly focusing on reference signals composed of non-periodic sequences. The authors propose an adaptive tracking control scheme integrating an adaptive controller with a stochastic approximation-type estimation algorithm. Different from the control scheme for Finite Impulse Response (FIR) systems, the estimation part not only estimates the unknown system parameters but also the unknown system outputs. Next, based on the certainty equivalent principle, the adaptive controller is designed using the above two estimates instead of the actual parameters and system outputs. To tackle the inherent coupling between the two estimates, the authors introduce a novel approach that combines the Lyapunov function method with a backward-shifted polynomial method featuring time-varying coefficients. This approach assists in establishing the mean square convergence of the estimates with a convergence rate of O(1k) under suitable conditions of the step size coefficient. Additionally, this paper shows that the designed adaptive control law can achieve asymptotically optimal tracking of non-periodic reference signals in the mean square sense. Finally, a numerical simulation is presented to validate the theoretical results obtained in this paper.</p>